The parameter estimation in CANDECOMP/PARAFAC (CP) is carried out by alternating least squares (ALS) that yields least-squares solutions and provides consistent outcomes. At the same time it has several drawbacks, like sensitivity to the presence of outliers in the data, issues with the computational efficiency in terms of processing time and memory requirements, as well as susceptibility to degeneracy conditions. These weaknesses have been addressed, but there is no outlier-robust procedure that at the same time is highly computationally efficient, especially for large data sets. A novel procedure based on an integrated estimation algorithm is proposed. This is an alternative to ALS, which guards against outliers and is computationally efficient at the same time. The performance of the new method is demonstrated on an extensive simulation study and an empirical example.

CANDECOMP/PARAFAC (CP) 中的参数估计通过交替最小二乘法 (ALS) 进行，产生最小二乘解并提供一致的结果。同时它也有一些缺点，例如对数据中异常值的存在敏感、处理时间和内存要求方面的计算效率问题以及对简并条件的敏感性。这些弱点已得到解决，但还没有一种既具有异常鲁棒性又具有高计算效率的程序，尤其是对于大型数据集。提出了一种基于集成估计算法的新程序。这是 ALS 的替代方案，可防止异常值，同时计算效率高。